An Unsupervised Ranking Consistency Approach based on Knowledge Base and Search Results
Date Issued
2016
Date
2016
Author(s)
Jiang, Jian-De
Abstract
Relevance ranking is the most important problem in web search system, such as Google, Yahoo!, Bing etc. Most of conventional approaches focus on optimizing ranking model by each query separately. One past work propose a two-stage supervised approach to improve relevance ranking by enhancing ranking consistency across queries with similar search intents. However, there are two crucial problems of previous work. First, they use pair-wise learning to rank to learn consistency, and the method relies on large-scale query log which only few of mature web search systems have. Most of developing search engines need to improve their performance without query log. Second, they considers query intents on entities in knowledge base. Nevertheless, entities cannot completely represent query intents because queries contains some specific information to ask, such as ``Kobe Bryant family'' for the intents of family. In this work, we propose an two-phase unsupervised approach to improve ranking consistency by knowledge base and search results. The first phase extracts consistency from search results and the second phase re-ranks search results by leveraging consistency and unique. Furthermore, we add query templates to help us clarify query intents completely. For the best of our knowledge, our work is the first unsupervised method with ranking consistency to improve relevance ranking. We conducted extensive experiments using Freebase and search results from Yahoo! search engine, and results demonstrate that our approach improves ranking consistency and relevance ranking significantly.
Subjects
Web Search
Ranking Consistency
Query Intent
Unsupervised Approach
Knowledge Base
Topical Cluster
Query Intent Template
Type
thesis
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